EP3503724B1 - Control of pests based on the prediction of infestation risks - Google Patents

Description

The present invention relates to the control of pests that can occur during the cultivation of crops. The control is carried out on the basis of the prediction of area-specific infestation risks.

WO 01/97097 A1 discloses a method for controlling pests in an agricultural field with crops. Treatment maps are generated that define the application of various products - such as seeds, fertilizers, crop protection products, etc. - to the field.

Martina Koller et al. ("Site-specific herbicide applications based on weed maps provide effective control", California Agriculture, July 1, 2005 (2005-07-01), pages 182-187, DOI: 10.3733/ca.v059n03p182, URL https://ucanr .edu/repositoryfiles/ca5903p182-69222.pdf ) reveals site-specific herbicide recommendation maps based on weed distribution maps, taking into account, among other things, the spatial distribution of weed seeds from previous years. CS Thomas et al. ("Utilization of GIS/GPS-Based Information Technology in Commercial Crop Decision Making in California, Washington, Oregon, Idaho, and Arizona", Journal of Nematology, September 1, 2002 (2002-09-01), pages 200-206, United States, URL: http://journals.fcla.edu/jon/article/viewFile/69406/67066 ) discloses the use of weather data, microclimate data, plant development stage data and geographical data to determine the risk of crops being infested with pests.

Current forecast models for the risk of pest or disease infestation are often based on weather data at field level or coarser spatial resolution.

The decision support system proPlant Expert (Newe et al. 2003, Johnen et al. 2010; www.proPlantexpert.com) uses data on crops (development stage, growth conditions, plant protection measures), weather (temperature, sunshine duration, wind speed, precipitation) and pests/diseases (economic thresholds, pest/disease pressure). With the help of this Based on the data, a field-specific risk of infestation is estimated and a recommendation is made regarding the timing of treatment, plant protection products and an evaluation of past plant protection measures.

Depending on the disease or pest, the current disease/pest pressure through inspection in the field, crop rotation, distance to a field with the same crop in the previous year and infestation in the previous year can be taken into account. The risk assessment can be carried out in different ways, depending on the disease or pest. For example, the underlying model can take development cycles or the migration of pests into account (Johnen et al. 2010). In general, this system is rule-based or heuristic in combination with process-based calculations (e.g. estimation of leaf moisture). The recommendations for action created in this way regarding the time of treatment and plant protection products are field-specific, but not area-specific.

Other decision support systems are known (Knight & Mumford 1994, Knight 1997).

In addition to such decision support systems, some of which include the modelling of a large number of pests or diseases, the literature contains a large number of models for the development of individual diseases or pests (Peterson & Higley 2001). These models can be rule- or process-based, as well as purely statistical or data-based/empirical. Furthermore, these models can be generic and can be used for several diseases or pests by adapting the model parameters, e.g. EPIWHEAT (Savary 2015) or WHEATPEST (Willocquet 2008).

In general, pure disease or pest infestation models can be used to estimate the risk of infestation (e.g. proPlant Expert), while coupled plant-disease or plant-pest models can also be used to estimate the influence of disease or pest on the development of crops, including yield (e.g. WHEATPEST, Willocquet 2008).

The decision support systems and models mentioned are usually so-called point models. The models are usually calibrated so that the output reflects the spatial representation of the input. If the input is collected at a point in space or If interpolation or estimation is made for a point in space, it is generally assumed that the model output is valid for the entire adjacent field. The application of so-called point models calibrated at field level to other, usually coarser scales is known (Hoffmann et al., 2016). Applying these so-called point models to several points within a field enables area-specific modeling of the infestation. However, spatial dependencies or spread vectors of an infestation are neglected here.

On the other hand, there are also systems for spatially explicit modeling of diseases and pests. These models explicitly simulate the temporal and spatial spread of diseases or pests. Spatial dependencies or spread vectors of an infestation are taken into account. One example is the combination of the systems APSIM (process-based simulation) and DYMEX (spatial modeling in 2d) (Parry et al., 2011).

In decision support systems (see above), forecast models are mainly used to estimate the time of an infestation and to take appropriate eradicative or curative plant protection measures.

Approaches to site-specific or variable application of pesticides are based on the currently observed biomass or leaf area, whereby more plant protection products (PPP) are generally applied as the biomass or leaf area increases. The additional effort can be achieved by a higher concentration of the active ingredient at the nozzle (mg/L) with the same application or by a higher application (L) with the same concentration of the active ingredient. These approaches take into account neither the local risk of diseases or pests nor the actual local disease/pest infestation.

Current methods estimate the general disease or pest pressure of a field using 1) scouting in the field, 2) sampling and analysis procedures (e.g. counting spores, genetic analysis in the laboratory to identify and/or quantify the fungi), 3) information on disease or pest risk factors in the previous year (e.g. distance to the field of the same crop in the previous year, or from the non-specific normalized and differentiated vegetation index (NDVI). Differentiated Vegetation Index" or "Normalized Density Vegetation Index") and 4) information on the actual extent of disease or pest infestation in the previous year.

1. 1) den aktuellen Befall effizient und bedarfsorientiert zu bekämpfen (bei Pilzbefall häufig auch kurativ, bevor das Pathogen sichtbar wird),
2. 2) ggf. biotische Vektoren langfristig zu schwächen (z.B. Schädlinge mit Wirtswechsel, Unkrautnester).

The current practicable methods are therefore not spatially differentiated or only slightly differentiated. In addition, they do not take long-term vectors into account. In particular, a combination of spatially high-resolution plant protection (risk prediction, application) taking such long-term vectors into account is not known. However, constant or long-term environmental factors (soil, field edge vegetation with woody plants, location, exposure) can lead to constant spatial disease or pest gradients or other spatial differences (partial areas). A spatially differentiated application is desirable here in order to

1. 1) to combat the current infestation efficiently and in a needs-oriented manner (in the case of fungal infestation, often also curatively, before the pathogen becomes visible),
2. 2) to weaken biotic vectors in the long term (e.g. pests with host change, weed nests).

This object is solved by the subject matter of independent claims 1 and 8. Preferred embodiments can be found in the dependent claims and in the following description.

A first object of the present invention is therefore a

1. (A) Erstellen einer ersten Befallskarte, wobei in der ersten Befallskarte Teilflächen in dem Feld verzeichnet sind, in denen in einer vergangenen Anbauperiode ein Befall mit Schadorganismen beobachtet worden ist, und
2. (B) Erstellen einer zweiten Befallskarte, wobei in der zweiten Befallskarte Teilflächen in dem Feld verzeichnet sind, in denen in der laufenden Anbauperiode ein Befall mit Schadorganismen beobachtet worden ist,
   und
3. (C) teilflächenspezifische Vorhersage des Befallsrisikos der Kulturpflanzen mit Schadorganismen für die laufende Anbauperiode, wobei die Vorhersage
   • auf Basis der ersten Befallskarte und auf Basis der zweiten Befallskarte und
   • auf Basis von teilflächenspezifischen Informationen zum Entwicklungsstand der Kulturpflanzen und/oder auf Basis von teilflächenspezifischen Umweltfaktoren und
   • auf Basis von Wetterinformationen,
     erfolgt,
     und
4. (D)Erstellen einer Behandlungskarte, wobei in der Behandlungskarte angegeben ist, welche Teilflächen des Feldes behandelt werden sollen, um die Ausbreitung von Schadorganismen zu verhindern und/oder Schadorganismen zu bekämpfen, wobei die Behandlungskarte
   • auf Basis der Vorhersage des Befallsrisikos für die laufende Anbauperiode erstellt, und
   • auf Basis der für den Zeitpunkt der geplanten Behandlung vorhergesehenen Bedingungen in Bezug auf Wetter, und/oder Entwicklungsstand der Kulturpflanze und/oder Umweltfaktoren erstellt und/oder angepasst wird,
     und
5. (E) Behandeln von Teilflächen des Feldes gemäß der Behandlungskarte,
   wobei die Vorhersage in Schritt (C) durch Modellierung der Ausbreitung von Schadorganismen mittels eines regel- oder prozessbasierten Modells erfolgt und die folgenden Parameter in die Modellierung einfließen:
   1. a) Wetter,
   2. b) Boden,
   3. c) Kulturpflanze,
   4. d) Kulturmaßnahmen,
   5. e) Entwicklungsstand der Kulturpflanze,
   6. f) aktueller Befall,
   7. g) Geographie.

A method for controlling pests in a field where crops are grown, the method comprising the following steps:

1. (A) preparing a first infestation map, the first infestation map showing parts of the field where infestation with pests has been observed in a previous growing season, and
2. (B) drawing up a second infestation map, the second infestation map showing parts of the field in which infestation with pests has been observed during the current growing season,
   and
3. (C) site-specific prediction of the risk of infestation of crops with pests for the current growing season, where the prediction
   • based on the first infestation map and on the second infestation map and
   • based on site-specific information on the development status of the crops and/or on site-specific environmental factors and
   • based on weather information,
     he follows,
     and
4. (D)Preparing a treatment map, which indicates which parts of the field are to be treated to prevent the spread of pests and/or to control pests, which treatment map
   • based on the prediction of the risk of infestation for the current growing season, and
   • is prepared and/or adapted on the basis of the conditions foreseen at the time of the planned treatment in terms of weather, and/or stage of development of the crop and/or environmental factors,
     and
5. (E) Treating parts of the field according to the treatment map,
   wherein the prediction in step (C) is made by modelling the spread of pests using a rule- or process-based model and the following parameters are included in the modelling:
   1. a) Weather,
   2. b) soil,
   3. c) cultivated plant,
   4. (d) cultural measures,
   5. e) stage of development of the crop,
   6. f) current infestation,
   7. g) Geography.

1. (A) eine erste Befallskarte, wobei in der ersten Befallskarte Teilflächen in dem Feld verzeichnet sind, in denen in einer vergangenen Anbauperiode ein Befall mit Schadorganismen beobachtet worden ist, und
2. (B) eine zweite Befallskarte, wobei in der zweiten Befallskarte Teilflächen in dem Feld verzeichnet sind, in denen in der laufenden Anbauperiode ein Befall mit Schadorganismen beobachtet worden ist,
   und
3. (C) Mittel zur Vorhersage des Befallsrisikos der Kulturpflanzen mit Schadorganismen für die laufende Anbauperiode, wobei die Vorhersage
   • auf Basis der ersten Befallskarte und auf Basis der zweiten Befallskarte,
     und
   • auf Basis von teilflächenspezifischen Informationen zum Entwicklungsstand der Kulturpflanzen und/oder auf Basis von teilflächenspezifischen Umweltfaktoren,
     und
   • auf Basis von Wetterinformationen,
     erfolgt,
     und
4. (D)Mittel zum Erstellen einer Behandlungskarte, wobei in der Behandlungskarte angegeben ist, welche Teilflächen des Feldes behandelt werden sollen, um die Ausbreitung von Schadorganismen zu verhindern und/oder Schadorganismen zu bekämpfen, wobei die Behandlungskarte
   • auf Basis der Vorhersage des Befallsrisikos für die laufende Anbauperiode erstellt,
     und
   • auf Basis der für den Zeitpunkt der geplanten Behandlung vorhergesehenen Bedingungen in Bezug auf Wetter, und/oder Entwicklungsstand der Kulturpflanze und/oder Umweltfaktoren erstellt und/oder angepasst wird,
     und
5. (E) Mittel zur Behandlung von Teilflächen des Feldes gemäß der Behandlungskarte, wobei die Vorhersage in Schritt (C) durch Modellierung der Ausbreitung von Schadorganismen mittels eines regel- oder prozessbasierten Modells erfolgt und die folgenden Parameter in die Modellierung einfließen:
   1. a) Wetter,
   2. b) Boden,
   3. c) Kulturpflanze,
   4. d) Kulturmaßnahmen,
   5. e) Entwicklungsstand der Kulturpflanze,
   6. f) aktueller Befall,
   7. g) Geographie.

Another object of the present invention is a system for controlling pests in a field in which crops are grown, comprising

1. (A) a first infestation map, the first infestation map showing parts of the field where infestation with pests has been observed in a previous growing season, and
2. (B) a second infestation map, the second infestation map showing parts of the field in which infestation by pests has been observed during the current growing season,
   and
3. (C) means of predicting the risk of pest infestation of crops for the current growing season, the prediction
   • based on the first infestation map and on the second infestation map,
     and
   • based on site-specific information on the development status of the crops and/or on site-specific environmental factors,
     and
   • based on weather information,
     he follows,
     and
4. (D)means for preparing a treatment map, the treatment map indicating which parts of the field are to be treated in order to prevent the spread of pests and/or to control pests, the treatment map
   • based on the prediction of the risk of infestation for the current growing season,
     and
   • is prepared and/or adapted on the basis of the conditions foreseen at the time of the planned treatment in terms of weather, and/or stage of development of the crop and/or environmental factors,
     and
5. (E) means for treating parts of the field according to the treatment map, wherein the prediction in step (C) is made by modelling the spread of pests using a rule- or process-based model and the following parameters are included in the modelling:
   1. a) Weather,
   2. b) soil,
   3. c) cultivated plant,
   4. (d) cultural measures,
   5. e) stage of development of the crop,
   6. f) current infestation,
   7. g) Geography.

The invention is explained in more detail below, without distinguishing between the subject matters of the invention (methods, systems, computer program product, treatment card, use). Rather, the following explanations should apply analogously to all subject matters of the invention, regardless of the context in which they occur (methods, systems, computer program product, treatment card, use).

Steps (C) and (D) of the method according to the invention are obligatory. Step (E) is obligatory and of steps (A) and (B) both steps ((A) and (B)) are obligatory. This therefore results in the following embodiment:
(A), (B), (C), (D), (E). The same applies to the system according to the invention.

In step (A) of the method according to the invention, a first infestation map is generated. The first infestation map shows locations on the field where pests have been observed in the past.

A "pest" is defined below as an organism that appears during the cultivation of crops and can damage the crop, negatively affect the crop's harvest or compete with the crop for natural resources. Examples of such pests are weeds, grass weeds, animal pests such as beetles, caterpillars and worms, fungi and pathogens (e.g. bacteria and viruses). Even if viruses are not biologically are among the organisms, they should nevertheless be considered as pests in this case.

The term "weeds" (plural: weeds) refers to plants of the spontaneous accompanying vegetation (segetal flora) in crop stands, grassland or gardens that are not deliberately grown there and that develop, for example, from the seed potential of the soil or via air travel. The term is not limited to herbs in the true sense of the word, but also includes grasses, ragweeds, mosses or woody plants.

In the field of plant protection, the term "weeds" (plural: weeds) is often used to clarify the distinction from herbaceous plants. In this text, the term weed is used as a generic term that is intended to include the term weeds, unless reference is made to specific weeds or weed grasses.

Weeds and grasses within the meaning of the present invention are therefore plants that accompany the cultivation of a desired crop. Since they compete with the crop for resources, they are undesirable and should therefore be controlled.

The term "field" refers to a spatially definable area of the earth's surface that is used for agricultural purposes, in which crops are planted, supplied with nutrients and harvested on such a field.

The term "cultivated plant" refers to a plant that is purposefully cultivated as a useful or ornamental plant through human intervention.

The infestation card is preferably a digital card. The term "digital" means that the card can be processed by a machine, usually a computer system. "Processing" refers to the well-known methods of electronic data processing (EDP).

Methods for generating digital maps showing locations where pests have occurred are described, for example, in GB2447681A , US6199000 , US2009/0132132A1 and WO00/23937 described.

Preferably, the pests were observed in the growing season immediately preceding the current growing season. In many cases, the growing season lasts one year, so the infestation map is usually created on the basis of the pest infestation observed in the previous year. However, it is also conceivable to incorporate information from growing seasons (years) further back in time into the creation of the first infestation map.

The use of such information is particularly advantageous when nests of pests have been observed in the field over a period of more than one growing season.

The term "nest" refers to a part of a field where a particular pest is repeatedly observed.

One example is field foxtail (Alopecurus myosuroides Huds), which disperses seeds close to the mother plant (Wilson & Brain, 1991). Here, the weed nests are stable or recurrent; however, new ones can also appear. Other examples are Orobranche crenata Forsk in field beans (Oveisi et al. 2010), Galium aparine, Viola arvensis Murr., Chenopodium album L., Polygonum aviculare L. (see overview Gerhards R (2010)).

Another example is the infestation by nematodes, which shows a spatially stable pattern (Campos-Herrera et al. 2011, Godefroid et al. 2013, Ortiz et al. 2010).

A "spatially stable pattern" refers to a repeatedly observable or measurable spatial distribution or arrangement of nests in a field. Furthermore, a spatially stable pattern of diseases and pests can refer to i) the cause of a disease or pest infestation, ii) the disease or pest infestation itself and iii) a characteristic of a disease or pest infestation. For example, a pest W can transmit a virus X that leads to a Disease Y leads to symptom Z. It is conceivable that W, X, Y and Z are measurable and each result in a stable pattern.

In particular, these patterns can be caused by an interaction between the development cycle of the pathogen or pest and other abiotic factors. The invention can therefore also be applied to regions in the field that, due to their nature, generally have a higher disease or pest pressure. Examples of such nature factors are location or exposure, hollows, soil or field edge nature (e.g. hedges).

One example is Septoria leaf blight, which occurs when conditions are favorable for infection by fungal spores of Septoria tritici. These favorable conditions can be caused by higher humidity or lower air exchange, due to exposure, local depressions and/or soil type.

An example of a pest that shows recurring patterns is the cabbage pod fly ( Dasineura brassicae ) on rapeseed. Due to its low ability to fly, the distance to the winter host is crucial for an infestation. A recurring pattern arises here from the location of the field relative to that of the winter host and to that of the field where rapeseed was grown in the previous year.

Another example is pathogens whose infestation pressure is determined by the rate of decomposition of plant residues in the soil. Stable nests can be caused by local differences in the soil.

The present invention is therefore preferably applicable to infestations with pests where nests or infestation gradients are observed in the field. If infestations have been observed at a location in the field in the past, there is a certain probability that infestations will occur again at that location.

However, the invention can also be applied to infestations with pests where no nests are observed. In such a case, the first infestation map is used to determine the factors that led to an observed infestation in the past. This involves determining which conditions on the infested areas, both before and during the infestation. By taking into account general knowledge about the pests and their spread, correlations can then be derived that can be used to predict the risk of infestation.

The first infestation map is used to predict the risk of (re-)infestation in the current growing season.

In step (C), a prediction is made of the risk of pests occurring in the field during the current growing season (risk of infestation).

The term "risk of infestation" refers to the probability of the occurrence of a disease, a pest or corresponding damage or symptoms on the crop, as well as the probability of the occurrence of other process-related disadvantages resulting from an infestation. In this case, the risk refers to a form of the disease, the pest or corresponding damage or symptoms on the plant, as well as to other process-related disadvantages resulting from it.

This risk is calculated. To do this, a model of the spread of the pests recorded in the first infestation map is preferably created.

Information about the infestation of the crops with pests in the current growing season is also included in the prediction. For this purpose, a second infestation map is created within the framework of the present invention (step (B)). The second infestation map shows locations in the field where pests have been observed in the current growing season.

The first and second infestation maps may be based on observations by a person (usually the farmer) or on data generated by sensors, or both.

Observations based on sensors are, for example, remote sensing data. Remote sensing data is digital information that is collected remotely, for example by Satellites were used to collect data from the earth's surface. The use of aircraft (unmanned (drones) or manned) to collect remote sensing data is also conceivable.

Appropriate remote sensing sensors are used to observe either the pests themselves (e.g. weeds and grasses) or their effects on the crops ( MS Moran et al.: Opportunities and Limitations for Image-Based Remote Sensing in Precision Crop Management, Remote Sensing of Environment (1997) 61: 319-346 ).

Sensors in the field or sensors attached to machines used to work the agricultural land (agricultural machinery) can also be used to monitor pests.

It is also conceivable to first calculate the infestation risk based on the first infestation map. If sub-areas are identified where there is currently a high risk of infestation, measures can be taken to verify or falsify a current infestation. It is conceivable, for example, that a farmer receives a computer-based message based on the prediction of a currently high risk of infestation, telling him to check the sub-areas for possible infestation. The check can be carried out visually, with the help of remote sensing sensors, sensors in the field or sensors on an agricultural machine. These observations then form the basis for the second infestation map. If an acute infestation is detected or not detected, these observations are incorporated as a second infestation map into the further (updated) prediction of the infestation risk.

Of course, it is also conceivable to create further infestation maps (a third, a fourth, etc.) during the current growing season and to incorporate them into the forecast. It is also conceivable to continuously monitor the infestation with pests using suitable sensors and to incorporate the relevant information into the (continuously updated) forecast.

For all infestation maps, the observation that no pests have been observed in a sub-area can also be valuable information that can be used to predict the risk of infestation.

Preferably, information on the stage of development of the crops is also included in the prediction of the risk of infestation. Depending on the stage of development, crops are more or less susceptible to infestation by a pest and/or pests only appear when a certain stage of development is reached.

The information on the development status of the crops includes, for example, the following information: development stage (e.g. BBCH code), growth height, leaf size, leaf shape, leaf colour, leaf area, sowing date, variety, existing biomass, nutritional status and health status.

The BBCH code (or BBCH scale) provides information about the morphological development stage of a plant. The abbreviation officially stands for the "Federal Biological Research Center, Federal Plant Variety Office and Chemical Industry". The BBCH scale is used in scientific communication to address questions of plant development and the optimal or recommended time to use fertilizer and plant protection measures in crop cultivation.

Information on the development status of crops can also be collected using remote sensing sensors and/or sensors in the field and/or human observation and/or sensors on agricultural machinery.

Information on site-specific environmental factors is also preferably included in the prediction of the infestation risk.

An "environmental factor" is an element of the environment that interacts with other elements (e.g. living things). Environmental factors can also be described as ecological factors or eco-factors.

The environmental factor is usually understood as a description of an environmental influence on an organism. This can benefit or harm the living being.

Environmental factors can be biotic or abiotic. Biotic factors are factors of the living environment, such as food, competitors, enemies, parasites and Pathogens. Abiotic factors are environmental conditions that are not caused or influenced by living organisms.

The forecast takes into account environmental factors that may influence the spread of the observed pests.

Preferred environmental factors are: soil type, soil condition, soil composition, soil moisture, crop rotation, soil nutrient content, field edge vegetation, exposure to solar radiation, wind and/or precipitation conditions, vegetation on neighboring fields and climate.

Preferably, information about the weather is also included in the prediction of the risk of infestation. The spread of many pests (particularly fungi and animal pests) depends in particular on the development of the weather during the cultivation period of the crop under consideration. The weather of the last days and weeks, months and years (past), as well as the current weather (present) and the expected weather (future) can be decisive for the occurrence and spread of pests. In this respect, information about the weather that is included in the prediction of the risk of infestation includes measured weather data from the current cultivation period, interpolated or extrapolated weather data and forecast weather data.

All information used to predict the risk of infestation should preferably have a spatial resolution that is higher than the field under consideration in order to enable a sub-area-specific prediction. The higher the spatial resolution, the smaller the sub-areas for which a specific prediction can be made.

The model for predicting the risk of infection can be a purely rule-based expert system that uses past observations to calculate the risk of infection for currently observed situations. On the other hand, the prediction model can also be mechanistic or process-based. The prediction model will usually be a mixture of rule-based and process-based systems. For example, leaf moisture can be estimated on a process-based basis and the resulting risk of infection can be estimated on a rule-based basis. Purely data-driven prediction models are also conceivable, e.g. based on neural networks. In principle, a combination of the model systems described above is conceivable.

Preferably, the infestation risk is simulated on a rule- or process-based basis if it is desired to reproduce the simulated processes (e.g. error analysis), if the understanding of the simulated processes is available in the form of corresponding equations, if the quality of the model results is superior to a purely data-driven model approach, or if the data basis collected is low.

On the other hand, a purely data-based approach (e.g. deep learning) can be used preferably if reproducing the simulated processes (e.g. error analysis) is insignificant, if the understanding of the simulated processes in the form of corresponding equations is lacking, if the quality of the model results is superior to a process- or rule-based model approach, or if the data basis collected is high. Depending on the model, the computing power required can also be decisive for the selection of a modeling approach.

In a first development step, an extension of a decision support system such as proPlant (see above) can be used for risk assessment. In a second development step, process-based or coupled models can be used for this purpose.

1. a) Wetter: tägliche Niederschlagssummen, Strahlungssummen, Tagesminimum und - maximum der Lufttemperatur sowie Temperatur in Bodennähe sowie Bodentemperatur, Windgeschwindigkeit, u.a.
2. b) Boden: Bodentyp, Bodentextur, Bodenart, Feldkapazität, Permanenter Welkepunkt, Organischer Kohlenstoff, mineralischer Stickstoffgehalt, Lagerungsdichte, Van-Genuchten-Parameter, u.a.
3. c) Kulturpflanze: Art, Sorte, sortenspezifische Parameter wie z.B. Spezifischer Blattflächenindex, Temperatursummen, maximale Wurzeltiefe, u.a.
4. d) Kulturmaßnahmen: Saatgut, Aussaattermin, Aussaatdichte, Aussaattiefe, Düngemittel, Düngemenge, Anzahl an Düngeterminen, Düngetermin, Bodenbearbeitung, Erntetermin, Ernterückstände, Pflanzenschutztermine, Planzenschutzmittel (Art und Ausbringungsmenge/-konzentration), Fruchtfolge, Distanz zum Feld gleicher Kultur im Vorjahr, Bewässerung, u.a.
5. e) Fernerkundung: Erfassung von Blattfläche o. Biomasse über Fernerkundungsprodukte (z.B. NDVI), Erfassung des Ernährungszustandes, Erfassen von Krankheiten, Erfassen der Wirksamkeit von Pflanzenschutzmaßnahmen, u.a.
6. f) Befall: Scouting-Parameter zum Schätzen des aktuellen Befallsdrucks oder Epizentrum der Krankheit, Scouting-Parameter zum Erfassen des tatsächlichen Befalls (z.B. Vorjahresbefall), Laborwerte zur Klassifizierung und/oder Quantifizierung der vorhandenen Schaderreger, u.a.
7. g) Geographie: Geographische Lage, Exposition, Orographie, Höhe, u.a.

The following parameters are included in the modelling

1. a) Weather: daily precipitation totals, radiation totals, daily minimum and maximum air temperature and temperature near the ground and ground temperature, wind speed, etc.
2. b) Soil: soil type, soil texture, soil type, field capacity, permanent wilting point, organic carbon, mineral nitrogen content, bulk density, Van Genuchten parameters, etc.
3. c) Crop: species, variety, variety-specific parameters such as specific leaf area index, temperature sums, maximum root depth, etc.
4. d) Cultural measures: seed, sowing date, sowing density, sowing depth, fertilizer, fertilizer quantity, number of fertilization dates, fertilization date, soil cultivation, harvest date, harvest residues, plant protection dates, Plant protection products (type and application rate/concentration), crop rotation, distance to the field of the same crop in the previous year, irrigation, etc.
5. e) Remote sensing: detection of leaf area or biomass using remote sensing products (e.g. NDVI), detection of nutritional status, detection of diseases, detection of the effectiveness of plant protection measures, etc.
6. f) Infestation: Scouting parameters to estimate the current infestation pressure or epicentre of the disease, scouting parameters to record the actual infestation (e.g. previous year's infestation), laboratory values to classify and/or quantify the pathogens present, etc.
7. g) Geography: geographical location, exposure, orography, altitude, etc.

The forecast is made on a site-specific basis (see e.g. Fig.1 ), i.e. the field is divided into sub-areas for which the respective infestation risk is calculated.

The division of the field into sub-areas can be done in such a way that the field is divided into a defined number of sub-areas of equal or almost equal size.

Preferably, the field is divided into sub-areas taking into account the spatial distribution of those factors that have the greatest influence on the spread of pests.

It is assumed that the soil conditions have a decisive influence on the spread. In this case, it may be useful to select the sub-areas in such a way that areas in the field with similar soil conditions are combined into one sub-area, while areas in the field that differ significantly in soil conditions are separated into different sub-areas.

The result of step (C) of the method according to the invention is a digital map of the field in which a risk of infestation with one or more pests is indicated for individual sub-areas. The risk of infestation is preferably indicated in the form of a percentage probability. If infestation with pests has been identified in some areas of the current cultivation period, for example when creating the second infestation map, If infestation has been observed in certain areas, the risk of infestation for these areas is 100%. However, if no infestation is currently observable, the risk of infestation is less than 100%.

Instead of the percentage probability of an infestation, or in addition to this, the infestation risk can also be shown in color on the digital map. For example, those areas for which a comparatively low risk of infestation has been determined (e.g. less than 20%) can be colored green, while those areas for which a comparatively high risk of infestation has been determined (e.g. greater than 80%) can be colored red.

Preferably, the risk of infestation is determined and displayed over time. This allows the user (e.g. farmer) to recognize when an increased risk is to be expected.

Preferably, the forecast is updated continuously, particularly taking into account current weather developments.

In step (D) of the method according to the invention, a site-specific treatment map is created on the basis of the site-specific prediction.

"Treatment" refers to those measures that prevent the occurrence or spread of harmful organisms and/or reduce the number of harmful organisms present. The treatment measures can relate to the crops, the environment of the crops (e.g. the soil) or the harmful organisms. In particular, when the measures relate to existing harmful organisms, the term "control" is also used here. The term "control" therefore refers to preventing the spread or reducing the number of harmful organisms present. In the case of weeds/grass weeds, for example, the term "quantity" refers to the biomass that is present in the form of weeds/grass weeds. However, the term "quantity" can also be understood, particularly in the case of a disease, to the number of crops that already show symptoms of disease.

Treatment is usually carried out by applying one or more plant protection products.

The term "plant protection product" refers to a product that can be used to effectively combat harmful organisms and/or prevent their spread. A plant protection product is usually a formulation that contains one or more active ingredients against one or more harmful organisms. If the harmful organisms are, for example, weeds or grass weeds, the active ingredient for combating the weeds or grass weeds is a herbicide and the plant protection product is a herbicide formulation. If the harmful organisms are, for example, a fungus, the active ingredient for combating the fungus is a fungicide and the plant protection product is a fungicide formulation. The term "control agent" is also used here as a synonym for the term "plant protection product".

It is also conceivable to apply growth regulators instead of or in combination with plant protection products if this is beneficial to the cultivation process and reduces the risk of infestation at field level or specific to a specific area. In this respect, growth regulators should also be included under the term plant protection products and the treatment of a partial area should also include the application of a growth regulator to the partial area.

The control of pests with appropriate chemical and/or biological plant protection products can also be supplemented or replaced by physical/mechanical control methods.

Physical removal (or mechanical removal) means that, for example, the weed/grass as a pest is either completely removed or parts of it are removed so that the weed/grass is no longer viable and dies. In contrast to controlling the weed/grass with a herbicide, which can be described as chemical control, no chemical or biological agent is applied in physical/mechanical control. Physical/mechanical control therefore does not exert any selection pressure on the weeds/grasses, but is often more complex and expensive than applying herbicides.

Physical/mechanical control should also be understood to mean, for example, irrigation, with which weeds are caused to grow in a desired manner in order to then eliminate them in a targeted manner. Physical/mechanical control should also be understood to include flaming of the pests.

For example, it is conceivable to use chemical and physical processes alternately.

It is also conceivable that part of a surface is treated chemically and another part physically.

However, a combined variant in one operation is also conceivable, e.g. if the use of chemicals is limited due to legal regulations or if there is a combination of pests where a combined control with mechanical and chemical means has the best chance of success. The combined use of physical and chemical methods can also be useful if the combination has a synergistic effect.

The treatment map indicates on which areas measures should be taken to prevent infestation by pests, to prevent the spread of existing pests or to reduce the number of existing pests.

If the treatment includes the application of one or more plant protection products, the term "application card" is used in addition to or instead of the term "treatment card".

The application map indicates on which parts of the field which quantities of one or more selected plant protection products are to be applied in order to prevent the spread of pests and/or to combat pests.

Preferably, the treatment card and the application card are digital cards.

It is conceivable that treatment should be carried out wherever pests have been observed during the current growing season.

It is also conceivable to provide for treatment in the treatment map only for those areas where the observed pests have exceeded a damage threshold.

"Damage threshold" is a term used in agriculture, forestry and horticulture. It indicates the level of infestation with pests, diseases or weeds at which control becomes economically viable. Up to this level, the additional economic cost of control is greater than the feared crop failure. If the infestation or weed infestation exceeds this level, the costs of control are at least offset by the expected additional yield.

Depending on the nature of a pest or disease, the damage threshold can vary greatly. In the case of pests or diseases that can only be controlled with great effort and with negative side effects for further production, the damage threshold can be very high. However, if even a small infestation can spread to a point that threatens to destroy the entire production, the damage threshold can be very low.

There are many examples in the state of the art for determining damage thresholds (see e.g. Claus M. Brodersen: Information in damage threshold models, reports of the GIL, volume 7, pages 26 to 36, http://www.gil-net.de/Publikationen/7_26.pdf ).

In many cases, it is necessary to combat pests preventively. This means that areas where a high risk of infestation with a pest has been identified are included in the application map as "areas to be treated with one or more plant protection products", even if no infestation is currently registered. In such a case, from an economic and/or ecological point of view, it is better to prevent an infestation from occurring in the first place than to wait until the infestation is acute and then combat it.

Accordingly, in one embodiment of the present invention, sub-areas are specified in the application map as "sub-areas to be treated with one or more plant protection products" for which an infestation risk threshold is exceeded.

In a preferred embodiment, the infestation risk threshold is determined in a similar manner to the damage thresholds. Preferably, the infestation risk of a partial area is equated with the percentage of the partial areas that are affected by 100% infestation. This will be explained using an example.

It is assumed that an infestation risk of 30% has been determined for a sub-area of 1 ha. To calculate the infestation risk threshold, the 30% infestation risk is converted into the theoretical case that 30% of the sub-area (1 ha x 30% = 0.3 ha) is infested with the pest and the rest (70%, corresponding to an area of 1 ha x 70% = 0.7 ha) is not infested. The costs for the treatment are the costs for treating the entire area (and not just the infested portion of 30%). It is now determined at what infestation risk it is more economical to treat the entire corresponding sub-area than to have to abandon the theoretically infested portion.

The application map indicates which areas are to be treated. It is also preferably indicated which plant protection product(s) are to be used to treat the respective areas.

In the simplest case, the application map is a so-called "on/off map", which simply indicates where one or more plant protection products should be used.

Preferably, the respective amount of the plant protection product to be used is specified. In some cases, more plant protection product is used as the biomass or leaf area of the cultivated plants increases. The additional expenditure can be achieved by a higher concentration of the active ingredient in the plant protection product, by a larger amount of plant protection product, or by both measures. In a preferred embodiment, both the amount of plant protection product and the concentration of the active ingredient are specified for the areas to be treated.

The application map shows the treatment of parts of the field at a point in time in the future. The application map should therefore preferably also include the values predicted for the time of application for those factors that influence the effectiveness of the plant protection product(s) used.

For example, it is conceivable that the temperature and/or soil moisture and/or the stage of development of the crop have an influence on the effectiveness. It is conceivable, for example, that an active ingredient is broken down more quickly at a higher temperature and higher soil moisture than at a lower temperature and lower soil moisture. It may therefore be that under certain conditions an increased amount of active ingredient is required to achieve a desired effect. The conditions for the time of application are predicted accordingly and the amount of plant protection product and/or concentration of the active ingredient in the plant protection product is adapted to these conditions.

It is also conceivable that the prevailing conditions are predicted for different points in the future in order to then decide which point is the most favorable for an application. For example, if conditions exist at a first point in time that require a higher amount of plant protection product than the conditions that exist at a second point in time, it may be economically more advantageous to carry out the application at the second point in time.

In a further preferred embodiment, the amount of plant protection product and/or the concentration of the active ingredient in the plant protection product is determined depending on the risk of infestation. If the risk of infestation is higher for a first partial area than for a second partial area, more plant protection product and/or a plant protection product with a higher concentration of active ingredient is applied to the first partial area than to the second partial area.

The selection of plant protection products is preferably made on the basis of all pests to be controlled. This means that a pesticide specifically suitable for each individual pest to be controlled is not necessarily selected. Rather, plant protection products are selected that are most suitable for the combination of pests present.

• Beispiel 1: Unkraut 1 und Unkraut 2 sind empfindlich gegenüber Herbizid 1 → Herbizid 1 wird appliziert.
• Beispiel 2: Unkraut 1 ist empfindlich gegenüber Herbizid 1 und Unkraut 2 ist empfindlich gegenüber Herbizid 2 und es ist kein einsetzbares Herbizid bekannt, das sowohl gegen Unkraut 1 als auch gegen Unkraut 2 wirkt → Herbizid 1 und Herbizid 2 werden appliziert.
• Beispiel 3: Unkraut 1 ist empfindlich gegenüber Herbizid 1 und Unkraut 2 ist empfindlich gegenüber Herbizid 2 und es ist ein einsetzbares Herbizid 3 bekannt, das gegen Unkraut 1 als auch Unkraut 2 wirkt → Es kann entweder Herbizid 3 allein oder Herbizid 1 in Kombination mit Herbizid 2 appliziert werden. Die Wahl erfolgt vorzugsweise auf Basis der effizienteren Bekämpfung.

This is illustrated by the following examples, in which two weeds (Weed 1 and Weed 2) occur on the same part of a field.

• Example 1: Weed 1 and Weed 2 are sensitive to Herbicide 1 → Herbicide 1 is applied.
• Example 2: Weed 1 is sensitive to herbicide 1 and weed 2 is sensitive to herbicide 2 and no usable herbicide is known that is effective against both weed 1 and weed 2 → herbicide 1 and herbicide 2 are applied.
• Example 3: Weed 1 is sensitive to herbicide 1 and weed 2 is sensitive to herbicide 2 and a usable herbicide 3 is known that is effective against weed 1 and weed 2 → Either herbicide 3 alone or herbicide 1 in combination with herbicide 2 can be applied. The choice is preferably made on the basis of the more effective control.

For example, the following control agents are used to combat the weed blackgrass: When applied in autumn from BBCH stage 11, a sulfonyl mixture consisting of the active ingredients mesosulfuron and iodusulforone (preferably plus safener) is used. When switching active ingredients, propoxycarbazone or pyroxsulam and florasulam are used. If the weed repeatedly exceeds the damage threshold in spring, then treatment with the active ingredients mesosulfuron and iodusulforone (plus safener) is also carried out in spring. In addition to the group of ALS inhibitors, there is also another group of active ingredients, the ACCase inhibitors with the so-called FOPS. The type of active ingredient and the application rate depend on the type of weed or grass, the number of plants per m ² or biomass of the weed or grass, and the degree of resistance.

• Die Applikationskarte ist eine on/off-Karte in Abhängigkeit des Befallrisikos.
• Die Applikationskarte ist eine on/off-Karte in Abhängigkeit des Befallsrisikos, wobei innerhalb einer "on"-Teilfläche variabel in Abhängigkeit der vorhandenen Biomasse appliziert wird.
• Die Applikationskarte enthält Anweisungen zur variablen Pflanzenschutzmittel-Dosierung direkt in Abhängigkeit des Befallsrisikos.
• Die Applikationskarte enthält Anweisungen zur variablen Pflanzenschutzmittel-Dosierung in Abhängigkeit der Biomasse, wobei die Dosen durch das vorliegende Befallsrisiko modifiziert werden.

Possible examples of application maps are:

• The application map is an on/off map depending on the infestation risk.
• The application map is an on/off map depending on the risk of infestation, whereby within an "on" sub-area the application is variable depending on the existing biomass.
• The application card contains instructions for variable pesticide dosage directly depending on the risk of infestation.
• The application card contains instructions for variable plant protection product dosage depending on the biomass, with the doses being modified by the existing infestation risk.

The term "dependency" usually means that there is a positive linear relationship: if there is twice the amount of biomass, twice the amount of PPP is applied; if the risk of infestation is twice as high, twice the amount of PPP is applied. However, it is also conceivable that there is a non-linear relationship.

In step (E) of the method according to the invention, partial areas are treated according to the treatment map created in step (D).

If an application map is created, one or more selected plant protection products are applied to the respective areas shown in the application map, thus combating the pests.

It is conceivable that the preferably digital application map contains commands for an application device for crop protection products. This means that the digital application map or parts thereof can be loaded into a working memory of an application device, from where the commands are transmitted to a spraying device.

An application device is understood to be a mechanical device for applying a plant protection agent to a field. Such an application device generally comprises at least one container for holding at least one plant protection agent, a spray device with which the plant protection agent is dispensed onto the field and a control device with which the conveyance of the at least one plant protection agent from its container towards the spray device is controlled. The digital application map is accordingly preferably stored in the working memory of the control unit. The control unit is available Furthermore, it is preferably connected to a positioning system which determines the position of the application device in the field. The control device preferably starts the application process when the digital application map indicates that an application is to be carried out at a location and when the positioning system reports that the application device is currently at this location.

In one embodiment, a person (user) loads the digital application map into a mobile computer system, e.g. a mobile phone (smartphone) that has a GPS receiver. While the user walks across the field, the mobile computer system uses a graphic image of the field to show him where he is and where he should manually spray (apply) one or more pesticides. He then sprays manually at the locations where the application map contains a corresponding indication. If the user applies a pesticide to a location, it is conceivable that a corresponding sensor system sends feedback to the mobile computer system about the application process that has taken place and that the application process is saved. It is also conceivable that the application process that has taken place is displayed on the mobile computer system so that the user can see where he has already applied. It is also conceivable that the data recorded on the mobile computer system is transmitted immediately or at a later point in time to a stationary computer system (e.g. a server) and saved there. In any case, the application carried out for each sub-area is recorded in the digital application map in such a way that the number N of applications (treatments) (still) to be carried out is reduced by one.

It is also conceivable that a person drives across the field in a vehicle, the respective position of the vehicle is recorded by means of a GPS receiver, and on the basis of the digital application map, commands are transmitted to a spraying device on the vehicle when the vehicle is at a location in the field where, according to the application map, an application of one or more pesticides is to take place, whereupon the corresponding application takes place automatically.

It is also conceivable that the application of one or more pesticides is fully automated: an unmanned machine moves across the field using GPS and applies the product to the locations in the field where a corresponding application is planned in the digital application map.

In particular, the invention can be used in combination with other or conventional application methods. One example is a uniform treatment of the entire field with a first application and a variable application according to this invention in a second application.

The invention is illustrated schematically by means of examples in Figures 1 and 2.

1. A: Einheitliches Risiko oder Befall;
2. B: Gradient ausgehend von einer Seite des Feldes oder jenseits des Feldes;
3. C: Gradient ausgehend von einer Ecke des Feldes oder jenseits des Feldes;
4. D: Gradient radial ausgehend von einem Epizentrum;
5. E: Gradient radial ausgehend von einem Epizentrum mit Drift;
6. F: Nest mit homogenem Risiko oder Befall;
7. G: Nest mit Gradient;
8. H: Zwei überlappende Nester mit jeweils homogenem Risiko oder Befall.

Fig. 1 : Presentation of selected examples of different spatially pronounced risk or infestation factors of diseases or pests in the field.

1. A: Uniform risk or infestation;
2. B: Gradient starting from one side of the field or beyond the field;
3. C: Gradient starting from a corner of the field or beyond the field;
4. D: Gradient radially from an epicenter;
5. E: Gradient radially from an epicenter with drift;
6. F: Nest with homogeneous risk or infestation;
7. G: Nest with gradient;
8. H: Two overlapping nests, each with homogeneous risk or infestation.

Other risk or infestation factors are possible (e.g. overlapping gradients of two disease factors, overlapping nests with gradients, multiple point risks, etc.)

Fig. 2: Schematic representation of the process according to the invention

In a first step, an infestation risk is estimated from current and historical data, including long-term risk vectors, using models.

In a second step, a corresponding application map is created.

In the example, above a risk limit, plant protection products are applied variably, within a lower risk range a minimum application dose is used, and below a lower limit no application is made (white tile in application map). The resolution of the application corresponds to the spatial resolution of the risk factors.

literature

• Hoffmann H, Zhao G, Asseng S, Bindi M, Biemath C, Constantin J, Coucheney E, Dechow R, Doro L, Eckersten H, Gaiser T, Grosz B, Heinlein F, Kassie BT, Kersebaum KC, Klein C, Kuhnert M, Lewan E, Moriondo M, Nendel C, Priesack E, Raynal H, Roggero PP, Rötter RP, Siebert S, Specka X, Tao F, Teixeira E, Trombi G, Wallach D, Weihermüller L, Yeluripati J, Ewert F. 2016. Impact of spatial soil and climate input data aggregation on regional yield simulations. PLoS ONE 11(4): e0151782. doi:10.1371/journal.pone.0151782 .
• Johnen A., Williams IH, Nilsson C., Klukowski Z., Luik A., Ulber B. 2010. The proPlant Decision Support System: Phenological Models for the Major Pests of Oilseed Rape and Their Key Parasitoids in Europe. In: Biocontrol-Based Integrated Management of Oilseed Rape Pests. Ed.: Ingrid H. Williams. Tartu 51014, Estonia. ISBN 978-90-481-3982-8. p. 381 - 403 .
• Knight JD 1997. The role of decision support systems in integrated crop protection. Agriculture, Ecosystems and Environment 64, 157-163 .
• Knight JD, Mumford JD 1994. Decision Support Systems in Crop Protection. Outlook on Agriculture 23, 281-285 .
• Newe M., Meier H., Johnen A., Volk T. 2003. proPlant expert.com - an online consultation system on crop protection in cereals, rape, potatoes and sugarbeet. EPPO Bulletin 33, 443-449 .
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Claims (8)

1. A method for the control of harmful organisms in a field in which crop plants are grown, wherein the method comprises the following steps:

   (A) creation of a first infestation map, wherein in the first infestation map subareas in the field are indicated in which an infestation with harmful organisms has been observed in a past cultivation period, and

   (B) creation of a second infestation map, wherein in the second infestation map subareas in the field are indicated in which an infestation with harmful organisms has been observed in the current cultivation period,
   and

   (C) subarea-specific prediction of the infestation risk to the crop plants with harmful organisms for the current cultivation period, wherein the prediction is made

   - on the basis of the first infestation map and on the basis of the second infestation map
   and

   - on the basis of subarea-specific information on the state of development of the crop plants and/or on the basis of subarea-specific environmental factors
   and

   - on the basis of weather information,
   and

   (D) creation of a treatment map, wherein in the treatment map it is stated which subareas of the field are to be treated, in order to prevent the dispersal of harmful organisms and/or to control harmful organisms, wherein the treatment map

   - is created on the basis of the prediction of the infestation risk for the current cultivation period,
   and

   - is created and/or adapted on the basis of the conditions predicted for the time of the planned treatment with regard to weather, and/or development state of the crop plant and/or environmental factors,
   and

   (E) treatment of subareas of the field in accordance with the treatment map,

   wherein the prediction in step (C) is made by modeling of the dispersal of harmful organisms by means of a rule- or process-based model and the following parameters influence the modeling:

   a) weather,

   b) soil,

   c) crop plant,

   d) cultivation measures,

   e) development state of the crop plant,

   f) current infestation,

   g) geography.
2. The method according to claim 1, wherein the subarea-specific prediction of the infestation risk is made on the basis of the following information:

   - on the basis of the first infestation map,

   - additionally on the basis of the second infestation map,
   and

   - on the basis of subarea-specific information on the state of development of the crop plants, or on the basis of subarea-specific environmental factors,
   and

   - on the basis of weather information.
3. The method according to one of claims 1 to 2, wherein the treatment in step (E) comprises an application of one or more plant protection agents.
4. The method according to one of claims 1 to 3, wherein in the treatment map a treatment is specified for those subareas for which the calculated infestation risk has exceeded a threshold value beyond which a treatment becomes economically useful.
5. The method according to one of claims 1 to 4, wherein a plant protection agent and a quantity and/or concentration of plant protection agent are stated in the treatment map for the subareas to be treated, wherein the quantity and/or concentration has been calculated as a function of the biomass present.
6. The method according to one of claims 1 to 4, wherein in the treatment map a plant protection agent and a quantity and/or concentration of plant protection agent are stated for the subareas to be treated, wherein the quantity and/or concentration has been calculated as a function of the infestation risk present in each case.
7. The method according to one of claims 1 to 4, wherein in the treatment map a plant protection agent and a quantity and/or concentration of plant protection agent are stated for the subareas to be treated, wherein the quantity and/or concentration has been calculated as a function of the infestation risk present in each case and as a function of the infestation risk present in each case.
8. A system for the control of harmful organisms in a field in which crop plants are grown, comprising

   (A) a first infestation map, wherein in the first infestation map subareas in the field are indicated in which an infestation with harmful organisms has been observed in a past cultivation period, and

   (B) a second infestation map, wherein in the second infestation map subareas in the field are indicated in which an infestation with harmful organisms has been observed in the current cultivation period,
   and

   (C) means for the prediction of the infestation risk to the crop plants with harmful organisms for the current cultivation period, wherein the prediction is made

   - on the basis of the first infestation map and on the basis of the second infestation map,
   and

   - on the basis of subarea-specific information on the state of development of the crop plants and/or on the basis of subarea-specific environmental factors,
   and

   - on the basis of weather information,
   and

   (D) means for the creation of a treatment map, wherein in the treatment map it is stated which subareas of the field are to be treated, in order to prevent the dispersal of harmful organisms and/or to control harmful organisms, wherein the treatment map

   - is created on the basis of the prediction of the infestation risk for the current cultivation period,
   and

   - is created and/or adapted on the basis of the conditions predicted for the time of the planned treatment with regard to weather, and/or development state of the crop plant and/or environmental factors,
   and

   (E) means for the treatment of subareas of the field in accordance with the treatment map,

   wherein the prediction in step (C) is made by modeling of the dispersal of harmful organisms by means of a rule- or process-based model and the following parameters influence the modeling:

   a) weather,

   b) soil,

   c) crop plant,

   d) cultivation measures,

   e) development state of the crop plant,

   f) current infestation,

   g) geography.

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